Graphics with ggplot: Your Turn Solutions

Load Libraries

Note: the ggplot package is contained within the tidyverse library.

library(tidyverse)

Graphics Intro

Make your first figure

1. Data set

head(diamonds)

carat	cut	color	clarity	depth	table	price	x	y	z
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75
0.24	Very Good	J	VVS2	62.8	57	336	3.94	3.96	2.48

2. Begin with the data

ggplot(data = diamonds)

3. Specify the aesthetic mappings

ggplot(data = diamonds, aes(x = carat, y = price))

4. Choose a geom

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point()

5. Add an aesthetic

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(aes(colour = cut))

6. Add another layer

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(aes(colour = cut), size = 2, alpha = .5) +
  geom_smooth()

7. Mapping aesthetics vs setting aesthetics

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(aes(colour = cut), size = 2, alpha = .5) +
  geom_smooth(aes(fill = cut), colour = "lightgrey") 

8. Coordinate transformations can be specified

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(aes(colour = cut), size = 2, alpha = .5) +
  geom_smooth(aes(fill = cut), colour = "lightgrey") +
  scale_y_log10()

9. Specify facet variables

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(aes(colour = cut), size = 2, alpha = .5) +
  geom_smooth(aes(fill = cut), colour = "lightgrey") +
  scale_y_log10() +
  facet_wrap(~cut)

Basics

Tidy Your Data

To tidy the preg table use pivot_longer() to create a long table.

preg <- tibble(pregnant = c("yes", "no"),
               male = c(NA, 10),
               female = c(20, 12))
preg

## # A tibble: 2 × 3
##   pregnant  male female
##   <chr>    <dbl>  <dbl>
## 1 yes         NA     20
## 2 no          10     12

Solution

preg_long <- preg %>%
  pivot_longer(cols = c("male", "female"),
               names_to = "sex",
               values_to = "count")
preg_long

## # A tibble: 4 × 3
##   pregnant sex    count
##   <chr>    <chr>  <dbl>
## 1 yes      male      NA
## 2 yes      female    20
## 3 no       male      10
## 4 no       female    12

Layers

Change the code below to have the points on top of the boxplots.

ggplot(data = mpg, aes(x = class, y = hwy)) + 
  geom_jitter() + 
  geom_boxplot()

Solution

ggplot(data = mpg, aes(x = class, y = hwy)) + 
  geom_boxplot() +
  geom_jitter() 

Perception

Diamonds

In the diamonds data, clarity and cut are ordinal, while price and carat are continuous.

Create a graphic that gives an overview of these four variables while respecting their types.

One possible plot, there will be many!

data(diamonds)
    
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point(aes(color = clarity)) + 
  geom_smooth(aes()) + 
  facet_grid(~cut)

Movies

The movies data set contains information from IMDB.com including ratings, genre, length in minutes, and year of release. Explore the differences in length, rating, etc. in movie genres over time. Hint: use faceting!

A few different plots, there will be many!

movies <- read.csv("https://unl-statistics.github.io/R-workshops/02-r-graphics/data/MovieSummary.csv")
summary(movies)

##        X             title                year     
##  Min.   :     7   Length:65134       Min.   :1893  
##  1st Qu.:144108   Class :character   1st Qu.:1954  
##  Median :195320   Mode  :character   Median :1983  
##  Mean   :208093                      Mean   :1975  
##  3rd Qu.:258227                      3rd Qu.:1998  
##  Max.   :411511                      Max.   :2005  
##                                                    
##      length           budget              rating      
##  Min.   :  1.00   Min.   :        0   Min.   : 1.000  
##  1st Qu.: 24.00   1st Qu.:   320000   1st Qu.: 5.300  
##  Median : 89.00   Median :  4000000   Median : 6.300  
##  Mean   : 73.36   Mean   : 15489887   Mean   : 6.138  
##  3rd Qu.:100.00   3rd Qu.: 20000000   3rd Qu.: 7.100  
##  Max.   :873.00   Max.   :200000000   Max.   :10.000  
##                   NA's   :58713                       
##      votes            mpaa              genre          
##  Min.   :     5   Length:65134       Length:65134      
##  1st Qu.:    12   Class :character   Class :character  
##  Median :    32   Mode  :character   Mode  :character  
##  Mean   :   768                                        
##  3rd Qu.:   131                                        
##  Max.   :157608                                        
## 

ggplot(movies, aes(x = year, y = budget, group = genre, color = genre)) + 
  geom_point()

ggplot(movies, aes(x = year, y = length, group = genre, color = genre)) +
  geom_smooth() 

ggplot(movies, aes(x = budget, y = rating, color = genre, group = genre)) + 
  geom_point() +
  geom_smooth()  + 
  facet_wrap(~mpaa)

ggplot(movies, aes(x = log(budget + 1), y = rating, color = genre, group = genre)) + 
  geom_point() +
  geom_smooth() 

ggplot(movies, aes(x = genre, fill = mpaa)) + 
  geom_bar() 

ggplot(movies, aes(x = rating, group = mpaa, fill = mpaa)) + 
  geom_density(alpha = .4) + 
  facet_wrap(~genre, nrow = 2)

Polishing Plots

Palmer Penguins

    install.packages("palmerpenguins")
    data(penguins, package = "palmerpenguins")
    head(penguins)

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Adelie	Torgersen	39.5	17.4	186	3800	female	2007
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	NA	NA	NA	NA	NA	2007
Adelie	Torgersen	36.7	19.3	193	3450	female	2007
Adelie	Torgersen	39.3	20.6	190	3650	male	2007

Meet the Palmer penguins & Bill Dimensions by Allison Horst

1. Create a scatterplot of bill length versus bill width from the penguins data, colored by species

p0 <- ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point()
    p0

2. Use the black and white theme

p1 <- p0 + 
  theme_bw()
p1

3. Clean up axis labels and include an informative title.

p2 <- p1 +
  scale_x_continuous("Bill Length (mm)") +
  scale_y_continuous("Bill Depth (mm)") +
  ggtitle("Palmer Penguins", subtitle = "Bill Size")
p2

4. Capitalize legend title and change the color palette from default.

p3 <- p2 +
  scale_color_viridis_d("Species")
p3

5. Move the legend to the bottom and set aspect ratio to 1.

p4 <- p3 + 
  theme(legend.position = "bottom",
        aspect.ratio = 1)
p4

6. Save your plot to a pdf file and open it in a pdf viewer.

Make sure you know where this is saving to; remember R projects and working directories!

ggsave(filename = "penguins.pdf", plot = p4)

7. Save a png of the same scatterplot.

ggsave(filename = "diamonds.png", plot = p4)

8. Embed the png into MS word or another editor.